Phoneme-level analysis using self-supervised embeddings identifies higher divergence in complex vowels and fricatives for emotional voice conversion deepfakes, enabling more interpretable detection across emotions.
Investigating the impact of speech enhancement on audio deep- fake detection in noisy environments
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
RADAR Challenge 2026 organizes a multilingual audio deepfake detection benchmark with media transformations, reporting participation from 33 development and 22 evaluation teams using EER metric.
citing papers explorer
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Phoneme-Level Deepfake Detection Across Emotional Conditions Using Self-Supervised Embeddings
Phoneme-level analysis using self-supervised embeddings identifies higher divergence in complex vowels and fricatives for emotional voice conversion deepfakes, enabling more interpretable detection across emotions.
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RADAR Challenge 2026: Robust Audio Deepfake Recognition under Media Transformations
RADAR Challenge 2026 organizes a multilingual audio deepfake detection benchmark with media transformations, reporting participation from 33 development and 22 evaluation teams using EER metric.